Cooperative resource sharing and cost minimization in energy hub systems using an improved grasshopper optimization algorithm approach

IF 4 3区 计算机科学 Q1 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE
Rui Fei, Jianwen Cui
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Abstract

This study presents a cooperative paradigm for energy hub systems (EHSs) where a network of interconnected hubs cooperates in exploiting the resources with the purpose of economic saving. In such an architecture, each hub provided with various sources of energy, such as combined heat and power (CHP), hot water tanks, renewable sources, electric chillers, and absorption chillers, will integrate all these sources for more adaptability and efficiency to the system. Moreover, the integration of energy storage systems (ESSs) is considered to enhance the flexibility of the energy hub concerning power, heating, and cooling. Recognizing the complexity associated with incorporating multiple constraints, the improved grasshopper optimization algorithm (IGOA) is introduced to effectively address this challenge. By leveraging this algorithm, the study aims to overcome the intricacies involved in considering various constraints and achieve an optimal outcome. The IGOA improves the efficiency and effectiveness of local and national searches in solving complex energy hub optimization problems. Reducing the likelihood of getting stuck in suboptimal solutions, enhances the algorithm's ability to find optimal solutions considering multiple constraints, thereby enhancing the overall performance and cost-effectiveness of EHSs. The issue is defined as a planning challenge, and by collaborative efforts, the expenses associated with the network energy hubs are reduced, illustrating the efficacy of this concept. The findings indicate the influence of the suggested cooperative technique, with operating cost reductions of 19.09 %, 13.27 %, and 8.75 % for Hub 1, Hub 2, and Hub 3, respectively. Furthermore, the cooperative framework eradicates energy deficits and disruptions, in contrast to 1,198.21 kWh of unfulfilled demand and 22 interruptions in the non-cooperative scenario. These results underscore the significant advantages of the collaborative technique in improving cost-efficiency, reliability, and resource utilization.
使用改进的蚱蜢优化算法方法实现能源枢纽系统中的合作资源共享和成本最小化
本研究提出了一种能源中枢系统(EHS)的合作模式,即由相互连接的中枢组成的网络以经济节约为目的,合作利用资源。在这种架构中,每个集线器都提供各种能源,如热电联产(CHP)、热水箱、可再生能源、电制冷机和吸收式制冷机,并将所有这些能源进行整合,以提高系统的适应性和效率。此外,储能系统(ESS)的集成也被认为可以提高能源枢纽在供电、供热和制冷方面的灵活性。由于认识到集成多个约束条件的复杂性,我们引入了改进的蚱蜢优化算法(IGOA)来有效应对这一挑战。通过利用该算法,本研究旨在克服考虑各种约束时所涉及的复杂性,并实现最优结果。在解决复杂的能源枢纽优化问题时,IGOA 提高了本地搜索和全国搜索的效率和效果。降低了陷入次优解的可能性,增强了算法在考虑多种约束条件的情况下找到最优解的能力,从而提高了 EHS 的整体性能和成本效益。该问题被定义为规划挑战,通过协同努力,减少了与网络能源枢纽相关的费用,说明了这一概念的功效。研究结果表明了所建议的合作技术的影响力,枢纽 1、枢纽 2 和枢纽 3 的运营成本分别降低了 19.09%、13.27% 和 8.75%。此外,合作框架消除了能源短缺和中断,而在非合作方案中,未满足的需求为 1,198.21 千瓦时,中断 22 次。这些结果凸显了合作技术在提高成本效益、可靠性和资源利用率方面的显著优势。
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来源期刊
Computers & Electrical Engineering
Computers & Electrical Engineering 工程技术-工程:电子与电气
CiteScore
9.20
自引率
7.00%
发文量
661
审稿时长
47 days
期刊介绍: The impact of computers has nowhere been more revolutionary than in electrical engineering. The design, analysis, and operation of electrical and electronic systems are now dominated by computers, a transformation that has been motivated by the natural ease of interface between computers and electrical systems, and the promise of spectacular improvements in speed and efficiency. Published since 1973, Computers & Electrical Engineering provides rapid publication of topical research into the integration of computer technology and computational techniques with electrical and electronic systems. The journal publishes papers featuring novel implementations of computers and computational techniques in areas like signal and image processing, high-performance computing, parallel processing, and communications. Special attention will be paid to papers describing innovative architectures, algorithms, and software tools.
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